We will load any relevant packages and set the default for our code visibility in the final report.
knitr::opts_chunk$set(echo = TRUE)
library(dataRetrieval)## Warning: package 'dataRetrieval' was built under R version 4.1.3## Warning: replacing previous import 'lifecycle::last_warnings' by
## 'rlang::last_warnings' when loading 'pillar'library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(zoo)## Warning: package 'zoo' was built under R version 4.1.3##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericlibrary(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(ggplot2)
library(plotly)## Warning: package 'plotly' was built under R version 4.1.2##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutlibrary(cowplot)##
## Attaching package: 'cowplot'## The following object is masked from 'package:lubridate':
##
## stamplibrary(tidyverse)## Warning: package 'tidyverse' was built under R version 4.1.3## Warning: replacing previous import 'lifecycle::last_warnings' by
## 'rlang::last_warnings' when loading 'hms'## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v tibble 3.1.6 v purrr 0.3.4
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## Warning: package 'tibble' was built under R version 4.1.3## Warning: package 'tidyr' was built under R version 4.1.3## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x lubridate::as.difftime() masks base::as.difftime()
## x lubridate::date() masks base::date()
## x plotly::filter() masks dplyr::filter(), stats::filter()
## x lubridate::intersect() masks base::intersect()
## x dplyr::lag() masks stats::lag()
## x lubridate::setdiff() masks base::setdiff()
## x cowplot::stamp() masks lubridate::stamp()
## x lubridate::union() masks base::union()library(fs)
library(Rcpp)
library(sp)
library(raster)##
## Attaching package: 'raster'## The following object is masked from 'package:tidyr':
##
## extract## The following object is masked from 'package:plotly':
##
## select## The following object is masked from 'package:dplyr':
##
## selectlibrary(rgdal)## rgdal: version: 1.5-23, (SVN revision 1121)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.2.1, released 2020/12/29
## Path to GDAL shared files: C:/Users/cwhee/Documents/R/win-library/4.1/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ runtime: Rel. 7.2.1, January 1st, 2021, [PJ_VERSION: 721]
## Path to PROJ shared files: C:/Users/cwhee/Documents/R/win-library/4.1/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.4-5
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading rgdal.
## Overwritten PROJ_LIB was C:/Users/cwhee/Documents/R/win-library/4.1/rgdal/projlibrary(rasterVis)## Loading required package: terra## terra version 1.3.4##
## Attaching package: 'terra'## The following object is masked from 'package:rgdal':
##
## project## The following object is masked from 'package:zoo':
##
## time<-## The following object is masked from 'package:dplyr':
##
## src## Loading required package: lattice## Warning: package 'lattice' was built under R version 4.1.3## Loading required package: latticeExtra##
## Attaching package: 'latticeExtra'## The following object is masked from 'package:ggplot2':
##
## layerlibrary(sf)## Linking to GEOS 3.9.0, GDAL 3.2.1, PROJ 7.2.1library(tidyverse)
library(dplyr)
library(EcoHydRology)## Loading required package: operators##
## Attaching package: 'operators'## The following object is masked from 'package:sf':
##
## %>%## The following object is masked from 'package:forcats':
##
## %>%## The following object is masked from 'package:stringr':
##
## %>%## The following object is masked from 'package:purrr':
##
## %>%## The following object is masked from 'package:tidyr':
##
## %>%## The following object is masked from 'package:tibble':
##
## %>%## The following object is masked from 'package:plotly':
##
## %>%## The following object is masked from 'package:dplyr':
##
## %>%## The following objects are masked from 'package:base':
##
## options, strrep## Loading required package: topmodel## Loading required package: DEoptim## Loading required package: parallel##
## DEoptim package
## Differential Evolution algorithm in R
## Authors: D. Ardia, K. Mullen, B. Peterson and J. Ulrich## Loading required package: XMLlibrary(dataRetrieval)
library(cowplot)
library(reshape2)##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithslibrary(EcoHydRology)
library(magrittr)##
## Attaching package: 'magrittr'## The following object is masked from 'package:operators':
##
## %>%## The following objects are masked from 'package:terra':
##
## extract, inset## The following object is masked from 'package:raster':
##
## extract## The following object is masked from 'package:purrr':
##
## set_names## The following object is masked from 'package:tidyr':
##
## extractlibrary(mapview)
library(tidyr)
library(readxl)
library(janitor)## Warning: package 'janitor' was built under R version 4.1.3##
## Attaching package: 'janitor'## The following object is masked from 'package:operators':
##
## %>%## The following object is masked from 'package:terra':
##
## crosstab## The following object is masked from 'package:raster':
##
## crosstab## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testKonza map and backgorund here
In this section, we will perform some basic data cleaning operations to get our data ready for further analysis
# bring in and examine konza groundwater data set
kgw <- read_csv("kgw.csv", col_types = cols(WLDate = col_character()))
head(kgw)## # A tibble: 6 x 36
## DATACODE RECTYPE Location Trans Plot Geology Recyear WLDate Elevation SWDate
## <chr> <dbl> <chr> <dbl> <dbl> <chr> <dbl> <chr> <chr> <chr>
## 1 AGW01 1 1-1AL 1 1 AL 1990 1990-0~ 364.47 na
## 2 AGW01 1 2-1Mor 2 1 Mor 1990 1990-0~ 365.44 na
## 3 AGW01 1 2-4Mor 2 4 Mor 1990 1990-0~ 365.28 na
## 4 AGW01 1 2-5Mor 2 5 Mor 1990 1990-0~ 365.18 na
## 5 AGW01 1 2-6Mor 2 6 Mor 1990 1990-0~ 365.26 na
## 6 AGW01 1 3-3Eis 3 3 Eis 1990 1990-0~ 371.87 na
## # ... with 26 more variables: Na1 <chr>, Na2 <chr>, K1 <chr>, K2 <chr>,
## # Li <chr>, NH4_N <chr>, Ca1 <chr>, Ca2 <chr>, Mg1 <chr>, Mg2 <chr>,
## # Sr <chr>, Ba <chr>, SO4 <chr>, F <chr>, Cl <chr>, NO3_N <chr>,
## # HPO4_P <chr>, Alkalinity <chr>, pH1 <chr>, DDB <chr>, pH2 <chr>,
## # Temp <chr>, Si1 <chr>, Si2 <chr>, B <chr>, Conduct <chr>names(kgw)## [1] "DATACODE" "RECTYPE" "Location" "Trans" "Plot"
## [6] "Geology" "Recyear" "WLDate" "Elevation" "SWDate"
## [11] "Na1" "Na2" "K1" "K2" "Li"
## [16] "NH4_N" "Ca1" "Ca2" "Mg1" "Mg2"
## [21] "Sr" "Ba" "SO4" "F" "Cl"
## [26] "NO3_N" "HPO4_P" "Alkalinity" "pH1" "DDB"
## [31] "pH2" "Temp" "Si1" "Si2" "B"
## [36] "Conduct"summary(kgw)## DATACODE RECTYPE Location Trans
## Length:7631 Min. :1 Length:7631 Min. :1.00
## Class :character 1st Qu.:1 Class :character 1st Qu.:2.00
## Mode :character Median :1 Mode :character Median :3.00
## Mean :1 Mean :2.89
## 3rd Qu.:1 3rd Qu.:4.00
## Max. :1 Max. :4.00
## NA's :10
## Plot Geology Recyear WLDate
## Min. :1.000 Length:7631 Min. :1990 Length:7631
## 1st Qu.:2.000 Class :character 1st Qu.:2000 Class :character
## Median :4.000 Mode :character Median :2008 Mode :character
## Mean :4.018 Mean :2007
## 3rd Qu.:6.000 3rd Qu.:2014
## Max. :7.000 Max. :2019
## NA's :10
## Elevation SWDate Na1 Na2
## Length:7631 Length:7631 Length:7631 Length:7631
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## K1 K2 Li NH4_N
## Length:7631 Length:7631 Length:7631 Length:7631
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Ca1 Ca2 Mg1 Mg2
## Length:7631 Length:7631 Length:7631 Length:7631
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Sr Ba SO4 F
## Length:7631 Length:7631 Length:7631 Length:7631
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Cl NO3_N HPO4_P Alkalinity
## Length:7631 Length:7631 Length:7631 Length:7631
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## pH1 DDB pH2 Temp
## Length:7631 Length:7631 Length:7631 Length:7631
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Si1 Si2 B Conduct
## Length:7631 Length:7631 Length:7631 Length:7631
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## # from the summary, it appears that all of the actual chem data is in string format, so we need to convert to numeric
kgw[,11:ncol(kgw)]<- lapply(kgw[,11:ncol(kgw)], as.numeric)## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion
## Warning in lapply(kgw[, 11:ncol(kgw)], as.numeric): NAs introduced by coercion#now convert date from character to proper format and clean up column names
kgw <- kgw %>%
mutate(date = lubridate::ymd(WLDate)) %>%
clean_names() %>%
dplyr::select(-wl_date)## Warning: 3877 failed to parse.# now look at data and col types again
head(kgw)## # A tibble: 6 x 36
## datacode rectype location trans plot geology recyear elevation sw_date na1
## <chr> <dbl> <chr> <dbl> <dbl> <chr> <dbl> <chr> <chr> <dbl>
## 1 AGW01 1 1-1AL 1 1 AL 1990 364.47 na NA
## 2 AGW01 1 2-1Mor 2 1 Mor 1990 365.44 na NA
## 3 AGW01 1 2-4Mor 2 4 Mor 1990 365.28 na NA
## 4 AGW01 1 2-5Mor 2 5 Mor 1990 365.18 na NA
## 5 AGW01 1 2-6Mor 2 6 Mor 1990 365.26 na NA
## 6 AGW01 1 3-3Eis 3 3 Eis 1990 371.87 na NA
## # ... with 26 more variables: na2 <dbl>, k1 <dbl>, k2 <dbl>, li <dbl>,
## # nh4_n <dbl>, ca1 <dbl>, ca2 <dbl>, mg1 <dbl>, mg2 <dbl>, sr <dbl>,
## # ba <dbl>, so4 <dbl>, f <dbl>, cl <dbl>, no3_n <dbl>, hpo4_p <dbl>,
## # alkalinity <dbl>, p_h1 <dbl>, ddb <dbl>, p_h2 <dbl>, temp <dbl>, si1 <dbl>,
## # si2 <dbl>, b <dbl>, conduct <dbl>, date <date>kgw_1 <- kgw %>%
dplyr::select(na1, cl, ca1, geology) %>%
tidyr::drop_na() %>%
mutate(geology = as_factor(geology))
library(plotly)
p <- plot_ly(kgw_1, x = ~na1, y = ~cl, z = ~ca1, color = ~geology) %>%
add_markers() %>%
layout(scene = list(xaxis = list(title = 'Na1+'),
yaxis = list(title = 'Ca1+'),
zaxis = list(title = 'Conductivity')))
p